During clinical diagnosis, the internal anatomy of a patient is imaged to determine how a certain disease has progressed. For example, the images may be used to help distinguish between infected tissues and healthy tissues within the patient. The images are also useful for radiotherapy treatment or planning or for surgical planning. Several modalities are used to generate images or functionality of anatomy of a patient which are suitable for diagnostic purposes or for therapy or surgical planning. Examples include conventional X-ray imaging, computed tomography (CT) imaging, magnetic resonance imaging (MRI) and nuclear medicine imaging techniques, such as positron emission tomography (PET) and single photon emission computer tomography (SPECT).
In the case of radiation treatment (RT) planning, CT imaging is generally used because image voxel gray values (measured in Hounsfield Units) can be used directly in the calculation of radiation dosage. Many of the current workflow and RT planning applications have been developed around CT imaging. However, a CT image's lack of soft tissue contrast creates ambiguous borders between organs, making it difficult for one to accurately contour organs on a CT image. Large inter-physician variability of manually segmented volumes has been observed. In addition, the precision of radiation therapy has evolved to a point such the uncertainty in segmentation has become the greatest source of error. For these reasons, demand for MR images acquired specifically for planning purposes is growing. There is also growing interest in applications that facilitate workflow with the MR images.
One such application is the automatic contouring of organs on MR images. Automatic segmentation is of extremely high importance in today's busy radiation therapy departments to achieve productivity requirements. Currently, clinicians (such as radiologists, dosimetrists or radiotherapists) must trace the outline of a few critical structures on a large number of images. Manually drawings the individual contours on a contiguous set of 2D slices and combining them to form 3D volumes is very time consuming and labor intensive. The time and labor increase significantly with the number of image slices in the image set, as well as the number and size of the organs, tumors, etc. in the anatomical area of interest. The quality of the contouring and the resulting 3D objects depend on the resolution and contrast of the 2D images and on the experience and judgment of the clinician performing the reconstruction. Automatic segmentation methods have been developed to address several of the problems with manual segmentation. However, automatic segmentation on MR images is more difficult than on CT images. Automatic segmentation of organs on MR images may be difficult because of the high inter-patient variability and the varying image quality.
It would be desirable to have an atlas-based system and method for automatically segmenting organs on MR images that provides improved performance including reliability and precision.